The epidemiology of severe Streptococcus pyogenes disease in Europe

Diseases caused by the Lancefield group A streptococcus, Streptococcus pyogenes, are amongst the most challenging to clinicians and public health specialists alike. Although severe infections caused by S. pyogenes are relatively uncommon, affecting around 3 per 100,000 of the population per annum in developed countries, the case fatality is high relative to many other infections. Despite a long scientific tradition of studying their occurrence and characteristics, many aspects of their epidemiology remain poorly understood, and potential control measures undefined. Epidemiological studies can play an important role in identifying host, pathogen and environmental factors associated with risk of disease, manifestation of particular syndromes or poor survival. This can be of value in targeting prevention activities, as well directing further basic research, potentially paving the way for the identification of novel therapeutic targets. The formation of a European network, Strep-EURO, provided an opportunity to explore epidemiological patterns across Europe. Funded by the Fifth Framework Programme of the European Commission s Directorate-General for Research (QLK2.CT.2002.01398), the Strep-EURO network was launched in September 2002. Twelve participants across eleven countries took part, led by the University of Lund in Sweden. Cases were defined as patients with S. pyogenes isolated from a normally sterile site, or non-sterile site in combination with clinical signs of streptococcal toxic shock syndrome (STSS). All participating countries undertook prospective enhanced surveillance between 1st January 2003 and 31st December 2004 to identify cases diagnosed during this period. A standardised surveillance dataset was defined, comprising demographic, clinical and risk factor information collected through a questionnaire. Isolates were collected by the national reference laboratories and characterised according to their M protein using conventional serological and emm gene typing. Descriptive statistics and multivariable analyses were undertaken to compare characteristics of cases between countries and identify factors associated with increased risk of death or development of STSS. Crude and age-adjusted rates of infection were calculated for each country where a catchment population could be defined. The project succeeded in establishing the first European surveillance network for severe S. pyogenes infections, with 5522 cases identified over the two years. Analysis of data gathered in the eleven countries yielded important new information on the epidemiology of severe S. pyogenes infections in Europe during the 2000s. Comprehensive epidemiological data on these infections were obtained for the first time from France, Greece and Romania. Incidence estimates identified a general north-south gradient, from high to low. Remarkably similar age-standardised rates were observed among the three Nordic participants, between 2.2 and 2.3 per 100,000 population. Rates in the UK were higher still, 2.9/100,000, elevated by an upsurge in drug injectors. Rates from these northern countries were reasonably close to those observed in the USA and Australia during this period. In contrast, rates of reports in the more central and southern countries (Czech Republic, Romania, Cyprus and Italy) were substantially lower, 0.3 to 1.5 per 100,000 population, a likely reflection of poorer uptake of microbiological diagnostic methods within these countries. Analysis of project data brought some new insights into risk factors for severe S. pyogenes infection, especially the importance of injecting drug users in the UK, with infections in this group fundamentally reshaping the epidemiology of these infections during this period. Several novel findings arose through this work, including the high degree of congruence in seasonal patterns between countries and the seasonal changes in case fatality rates. Elderly patients, those with compromised immune systems, those who developed STSS and those infected with an emm/M78, emm/M5, emm/M3 or emm/M1 were found to be most likely to die as a result of their infection, whereas those diagnosed with cellulitis, septic arthritis, puerperal sepsis or with non-focal infection were associated with low risk of death, as were infections occurring during October. Analysis of augmented data from the UK found use of NSAIDs to be significantly associated with development of STSS, adding further fuel to the debate surrounding the role of NSAIDs in the development of severe disease. As a largely community-acquired infection, occurring sporadically and diffusely throughout the population, opportunities for control of severe infections caused by S. pyogenes remain limited, primarily involving contact chemoprophylaxis where clusters arise. Analysis of UK Strep-EURO data were used to quantify the risk to household contacts of cases, forming the basis of national guidance on the management of infection. Vaccines currently under development could offer a more effective control programme in future. Surveillance of invasive infections caused by S. pyogenes is of considerable public health importance as a means of identifying long and short-term trends in incidence, allowing the need for, or impact of, public health measures to be evaluated. As a dynamic pathogen co-existing among a dynamic population, new opportunities for exploitation of its human host are likely to arise periodically, and as such continued monitoring remains essential.

Language and Letters of the Bluestocking Network : Sociolinguistic Issues in Eighteenth Century Epistolary English

This study deals with language change and variation in the correspondence of the eighteenth-century Bluestocking circle, a social network which provided learned men and women with an informal environment for the pursuit of scholarly entertainment. Elizabeth Montagu (1718 1800), a notable social hostess and a Shakespearean scholar, was one of their key figures. The study presents the reconstruction of Elizabeth Montagu s social networks from her youth to her later years with a special focus on the Bluestocking circle, and linguistic research on private correspondence between Montagu and her Bluestocking friends and family members between the years 1738 1778. The epistolary language use is investigated using the methods and frameworks of corpus linguistics, historical sociolinguistics, and social network analysis. The approach is diachronic and concerns real-time language change. The research is based on a selection of manuscript letters which I have edited and compiled into an electronic corpus (Bluestocking Corpus). I have also devised a network strength scale in order to quantify the strength of network ties and to compare the results of the linguistic research with the network analysis. The studies range from the reconstruction and analysis of Elizabeth Montagu s most prominent social networks to the analysis of changing morphosyntactic features and spelling variation in Montagu s and her network members correspondence. The linguistic studies look at the use of the progressive construction, preposition stranding and pied piping, and spelling variation in terms of preterite and past participle endings in the regular paradigm (-ed, - d, -d, - t, -t) and full / contracted spellings of auxiliary verbs. The results are analysed in terms of social network membership, sociolinguistic variables of the correspondents, and, when relevant, aspects of eighteenth-century linguistic prescriptivism. The studies showed a slight diachronic increase in the use of the progressive, a significant decrease of the stigmatised preposition stranding and increase of pied piping, and relatively informal but socially controlled epistolary spelling. Certain significant changes in Elizabeth Montagu s language use over the years could be attributed to her increasingly prominent social standing and the changes in her social networks, and the strength of ties correlated strongly with the use of the progressive in the Bluestocking Corpus. Gender, social rank, and register in terms of kinship/friendship had a significant influence in language use, and an effect of prescriptivism could also be detected. Elizabeth Montagu s network ties resulted in language variation in terms of network membership, her own position in a given network, and the social factors that controlled eighteenth-century interaction. When all the network ties are strong, linguistic variation seems to be essentially linked to the social variables of the informants.

Cellular and network mechanisms generating spontaneous population events in the immature rat hippocampus

Distinct endogenous network events, generated independently of sensory input, are a general feature of various structures of the immature central nervous system. In the immature hippocampus, these type of events are seen as "giant depolarizing potentials" (GDPs) in intracellular recordings in vitro. GABA, the major inhibitory neurotransmitter of the adult brain, has a depolarizing action in immature neurons, and GDPs have been proposed to be driven by GABAergic transmission. Moreover, GDPs have been thought to reflect an early pattern that disappears during development in parallel with the maturation of hyperpolarizing GABAergic inhibition. However, the adult hippocampus in vivo also generates endogenous network events known as sharp (positive) waves (SPWs), which reflect synchronous discharges of CA3 pyramidal neurons and are thought to be involved in cognitive functions. In this thesis, mechanisms of GDP generation were studied with intra- and extracellular recordings in the neonatal rat hippocampus in vitro and in vivo. Immature CA3 pyramidal neurons were found to generate intrinsic bursts of spikes and to act as cellular pacemakers for GDP activity whereas depolarizing GABAergic signalling was found to have a temporally non-patterned facilitatory role in the generation of the network events. Furthermore, the data indicate that the intrinsic bursts of neonatal CA3 pyramidal neurons and, consequently, GDPs are driven by a persistent Na+ current and terminated by a slow Ca2+-dependent K+ current. Gramicidin-perforated patch recordings showed that the depolarizing driving force for GABAA receptor-mediated actions is provided by Cl- uptake via the Na-K-C1 cotransporter, NKCC1, in the immature CA3 pyramids. A specific blocker of NKCC1, bumetanide, inhibited SPWs and GDPs in the neonatal rat hippocampus in vivo and in vitro, respectively. Finally, pharmacological blockade of the GABA transporter-1 prolonged the decay of the large GDP-associated GABA transients but not of single postsynaptic GABAA receptor-mediated currents. As a whole the data in this thesis indicate that the mechanism of GDP generation, based on the interconnected network of bursting CA3 pyramidal neurons, is similar to that involved in adult SPW activity. Hence, GDPs do not reflect a network pattern that disappears during development but they are the in vitro counterpart of neonatal SPWs.

VEGFR-3 and Tie pathways in vascular network formation

The blood and lymphatic vascular systems are essential for life, but they may become harnessed for sinister purposes in pathological conditions. For example, tumors learn to grow a network of blood vessels (angiogenesis), securing a source of oxygen and nutrients for sustained growth. On the other hand, damage to the lymph nodes and the collecting lymphatic vessels may lead to lymphedema, a debilitating condition characterized by peripheral edema and susceptibility to infections. Promoting the growth of new lymphatic vessels (lymphangiogenesis) is an attractive approach to treat lymphedema patients. Angiopoietin-1 (Ang1), a ligand for the endothelial receptor tyrosine kinases Tie1 and Tie2. The Ang1/Tie2 pathway has previously been implicated in promoting endothelial stability and integrity of EC monolayers. The studies presented here elucidate a novel function for Ang1 as a lymphangiogenic factor. Ang1 is known to decrease the permeability of blood vessels, and could thus act as a more global antagonist of plasma leakage and tissue edema by promoting growth of lymphatic vessels and thereby facilitating removal of excess fluid and other plasma components from the interstitium. These findings reinforce the idea that Ang1 may have therapeutic value in conditions of tissue edema. VEGFR-3 is present on all endothelia during development, but in the adult its expression becomes restricted to the lymphatic endothelium. VEGF-C and VEGF-D are ligands for VEGFR-3, and potently promote lymphangiogenesis in adult tissues, with direct and remarkably specific effects on the lymphatic endothelium in adult tissues. The data presented here show that VEGF-C and VEGF-D therapy can restore collecting lymphatic vessels in a novel orthotopic model of breast cancer-related lymphedema. Furthermore, the study introduces a novel approach to improve VEGF-C/VEGF-D therapy by using engineered heparin-binding forms of VEGF-C, which induced the rapid formation of organized lymphatic vessels. Importantly, VEGF-C therapy also greatly improved the survival and integration of lymph node transplants. The combination of lymph node transplantation and VEGF-C therapy provides a basis for future therapy of lymphedema. In adults, VEGFR-3 expression is restricted to the lymphatic endothelium and the fenestrated endothelia of certain endocrine organs. These results show that VEGFR-3 is induced at the onset of angiogenesis in the tip cells that lead the formation of new vessel sprouts, providing a tumor-specific vascular target. VEGFR-3 acts downstream of VEGF/VEGFR-2 signals, but, once induced, can sustain angiogenesis when VEGFR-2 signaling is inhibited. The data presented here implicate VEGFR-3 as a novel regulator of sprouting angiogenesis along with its role in regulating lymphatic vessel growth. Targeting VEGFR-3 may provide added efficacy to currently available anti-angiogenic therapeutics, which typically target the VEGF/VEGFR-2 pathway.

Automatic Lameness Detection in a Milking Robot : Instrumentation, measurement software, algorithms for data analysis and a neural network model

The aim of this thesis is to develop a fully automatic lameness detection system that operates in a milking robot. The instrumentation, measurement software, algorithms for data analysis and a neural network model for lameness detection were developed. Automatic milking has become a common practice in dairy husbandry, and in the year 2006 about 4000 farms worldwide used over 6000 milking robots. There is a worldwide movement with the objective of fully automating every process from feeding to milking. Increase in automation is a consequence of increasing farm sizes, the demand for more efficient production and the growth of labour costs. As the level of automation increases, the time that the cattle keeper uses for monitoring animals often decreases. This has created a need for systems for automatically monitoring the health of farm animals. The popularity of milking robots also offers a new and unique possibility to monitor animals in a single confined space up to four times daily. Lameness is a crucial welfare issue in the modern dairy industry. Limb disorders cause serious welfare, health and economic problems especially in loose housing of cattle. Lameness causes losses in milk production and leads to early culling of animals. These costs could be reduced with early identification and treatment. At present, only a few methods for automatically detecting lameness have been developed, and the most common methods used for lameness detection and assessment are various visual locomotion scoring systems. The problem with locomotion scoring is that it needs experience to be conducted properly, it is labour intensive as an on-farm method and the results are subjective. A four balance system for measuring the leg load distribution of dairy cows during milking in order to detect lameness was developed and set up in the University of Helsinki Research farm Suitia. The leg weights of 73 cows were successfully recorded during almost 10,000 robotic milkings over a period of 5 months. The cows were locomotion scored weekly, and the lame cows were inspected clinically for hoof lesions. Unsuccessful measurements, caused by cows standing outside the balances, were removed from the data with a special algorithm, and the mean leg loads and the number of kicks during milking was calculated. In order to develop an expert system to automatically detect lameness cases, a model was needed. A probabilistic neural network (PNN) classifier model was chosen for the task. The data was divided in two parts and 5,074 measurements from 37 cows were used to train the model. The operation of the model was evaluated for its ability to detect lameness in the validating dataset, which had 4,868 measurements from 36 cows. The model was able to classify 96% of the measurements correctly as sound or lame cows, and 100% of the lameness cases in the validation data were identified. The number of measurements causing false alarms was 1.1%. The developed model has the potential to be used for on-farm decision support and can be used in a real-time lameness monitoring system.

Data mining for telecommunications network log analysis

Telecommunications network management is based on huge amounts of data that are continuously collected from elements and devices from all around the network. The data is monitored and analysed to provide information for decision making in all operation functions. Knowledge discovery and data mining methods can support fast-pace decision making in network operations. In this thesis, I analyse decision making on different levels of network operations. I identify the requirements decision-making sets for knowledge discovery and data mining tools and methods, and I study resources that are available to them. I then propose two methods for augmenting and applying frequent sets to support everyday decision making. The proposed methods are Comprehensive Log Compression for log data summarisation and Queryable Log Compression for semantic compression of log data. Finally I suggest a model for a continuous knowledge discovery process and outline how it can be implemented and integrated to the existing network operations infrastructure.

Middleware for Mobile Sensing Applications in Urban Environments

Sensor networks represent an attractive tool to observe the physical world. Networks of tiny sensors can be used to detect a fire in a forest, to monitor the level of pollution in a river, or to check on the structural integrity of a bridge. Application-specific deployments of static-sensor networks have been widely investigated. Commonly, these networks involve a centralized data-collection point and no sharing of data outside the organization that owns it. Although this approach can accommodate many application scenarios, it significantly deviates from the pervasive computing vision of ubiquitous sensing where user applications seamlessly access anytime, anywhere data produced by sensors embedded in the surroundings. With the ubiquity and ever-increasing capabilities of mobile devices, urban environments can help give substance to the ubiquitous sensing vision through Urbanets, spontaneously created urban networks. Urbanets consist of mobile multi-sensor devices, such as smart phones and vehicular systems, public sensor networks deployed by municipalities, and individual sensors incorporated in buildings, roads, or daily artifacts. My thesis is that "multi-sensor mobile devices can be successfully programmed to become the underpinning elements of an open, infrastructure-less, distributed sensing platform that can bring sensor data out of their traditional close-loop networks into everyday urban applications". Urbanets can support a variety of services ranging from emergency and surveillance to tourist guidance and entertainment. For instance, cars can be used to provide traffic information services to alert drivers to upcoming traffic jams, and phones to provide shopping recommender services to inform users of special offers at the mall. Urbanets cannot be programmed using traditional distributed computing models, which assume underlying networks with functionally homogeneous nodes, stable configurations, and known delays. Conversely, Urbanets have functionally heterogeneous nodes, volatile configurations, and unknown delays. Instead, solutions developed for sensor networks and mobile ad hoc networks can be leveraged to provide novel architectures that address Urbanet-specific requirements, while providing useful abstractions that hide the network complexity from the programmer. This dissertation presents two middleware architectures that can support mobile sensing applications in Urbanets. Contory offers a declarative programming model that views Urbanets as a distributed sensor database and exposes an SQL-like interface to developers. Context-aware Migratory Services provides a client-server paradigm, where services are capable of migrating to different nodes in the network in order to maintain a continuous and semantically correct interaction with clients. Compared to previous approaches to supporting mobile sensing urban applications, our architectures are entirely distributed and do not assume constant availability of Internet connectivity. In addition, they allow on-demand collection of sensor data with the accuracy and at the frequency required by every application. These architectures have been implemented in Java and tested on smart phones. They have proved successful in supporting several prototype applications and experimental results obtained in ad hoc networks of phones have demonstrated their feasibility with reasonable performance in terms of latency, memory, and energy consumption.

The Most Probable Bayesian Network and Beyond

This doctoral dissertation introduces an algorithm for constructing the most probable Bayesian network from data for small domains. The algorithm is used to show that a popular goodness criterion for the Bayesian networks has a severe sensitivity problem. The dissertation then proposes an information theoretic criterion that avoids the problem.

Optimisation Problems in Wireless Sensor Networks : Local Algorithms and Local Graphs

This thesis studies optimisation problems related to modern large-scale distributed systems, such as wireless sensor networks and wireless ad-hoc networks. The concrete tasks that we use as motivating examples are the following: (i) maximising the lifetime of a battery-powered wireless sensor network, (ii) maximising the capacity of a wireless communication network, and (iii) minimising the number of sensors in a surveillance application. A sensor node consumes energy both when it is transmitting or forwarding data, and when it is performing measurements. Hence task (i), lifetime maximisation, can be approached from two different perspectives. First, we can seek for optimal data flows that make the most out of the energy resources available in the network; such optimisation problems are examples of so-called max-min linear programs. Second, we can conserve energy by putting redundant sensors into sleep mode; we arrive at the sleep scheduling problem, in which the objective is to find an optimal schedule that determines when each sensor node is asleep and when it is awake. In a wireless network simultaneous radio transmissions may interfere with each other. Task (ii), capacity maximisation, therefore gives rise to another scheduling problem, the activity scheduling problem, in which the objective is to find a minimum-length conflict-free schedule that satisfies the data transmission requirements of all wireless communication links. Task (iii), minimising the number of sensors, is related to the classical graph problem of finding a minimum dominating set. However, if we are not only interested in detecting an intruder but also locating the intruder, it is not sufficient to solve the dominating set problem; formulations such as minimum-size identifying codes and locating dominating codes are more appropriate. This thesis presents approximation algorithms for each of these optimisation problems, i.e., for max-min linear programs, sleep scheduling, activity scheduling, identifying codes, and locating dominating codes. Two complementary approaches are taken. The main focus is on local algorithms, which are constant-time distributed algorithms. The contributions include local approximation algorithms for max-min linear programs, sleep scheduling, and activity scheduling. In the case of max-min linear programs, tight upper and lower bounds are proved for the best possible approximation ratio that can be achieved by any local algorithm. The second approach is the study of centralised polynomial-time algorithms in local graphs these are geometric graphs whose structure exhibits spatial locality. Among other contributions, it is shown that while identifying codes and locating dominating codes are hard to approximate in general graphs, they admit a polynomial-time approximation scheme in local graphs.

Where and how to conserve: Extending the scope of spatial reserve network design

Ongoing habitat loss and fragmentation threaten much of the biodiversity that we know today. As such, conservation efforts are required if we want to protect biodiversity. Conservation budgets are typically tight, making the cost-effective selection of protected areas difficult. Therefore, reserve design methods have been developed to identify sets of sites, that together represent the species of conservation interest in a cost-effective manner. To be able to select reserve networks, data on species distributions is needed. Such data is often incomplete, but species habitat distribution models (SHDMs) can be used to link the occurrence of the species at the surveyed sites to the environmental conditions at these locations (e.g. climatic, vegetation and soil conditions). The probability of the species occurring at unvisited location is next predicted by the model, based on the environmental conditions of those sites. The spatial configuration of reserve networks is important, because habitat loss around reserves can influence the persistence of species inside the network. Since species differ in their requirements for network configuration, the spatial cohesion of networks needs to be species-specific. A way to account for species-specific requirements is to use spatial variables in SHDMs. Spatial SHDMs allow the evaluation of the effect of reserve network configuration on the probability of occurrence of the species inside the network. Even though reserves are important for conservation, they are not the only option available to conservation planners. To enhance or maintain habitat quality, restoration or maintenance measures are sometimes required. As a result, the number of conservation options per site increases. Currently available reserve selection tools do however not offer the ability to handle multiple, alternative options per site. This thesis extends the existing methodology for reserve design, by offering methods to identify cost-effective conservation planning solutions when multiple, alternative conservation options are available per site. Although restoration and maintenance measures are beneficial to certain species, they can be harmful to other species with different requirements. This introduces trade-offs between species when identifying which conservation action is best applied to which site. The thesis describes how the strength of such trade-offs can be identified, which is useful for assessing consequences of conservation decisions regarding species priorities and budget. Furthermore, the results of the thesis indicate that spatial SHDMs can be successfully used to account for species-specific requirements for spatial cohesion - in the reserve selection (single-option) context as well as in the multi-option context. Accounting for the spatial requirements of multiple species and allowing for several conservation options is however complicated, due to trade-offs in species requirements. It is also shown that spatial SHDMs can be successfully used for gaining information on factors that drive a species spatial distribution. Such information is valuable to conservation planning, as better knowledge on species requirements facilitates the design of networks for species persistence. This methods and results described in this thesis aim to improve species probabilities of persistence, by taking better account of species habitat and spatial requirements. Many real-world conservation planning problems are characterised by a variety of conservation options related to protection, restoration and maintenance of habitat. Planning tools therefore need to be able to incorporate multiple conservation options per site, in order to continue the search for cost-effective conservation planning solutions. Simultaneously, the spatial requirements of species need to be considered. The methods described in this thesis offer a starting point for combining these two relevant aspects of conservation planning.